Date: (Thu) Jan 14, 2016

Introduction:

Data: Source: Training: https://www.kaggle.com/c/facial-keypoints-detection/download/training.zip
New: https://www.kaggle.com/c/facial-keypoints-detection/download/test.zip
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<pointer>"; if url specifies a zip file, name = "<filename>"
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(name = "Faces_patch_mean_Train.csv") 

glbObsNewFile <- list(name = "Faces_patch_mean_Test.csv") # default OR
    #list(splitSpecs = list(method = NULL #select from c(NULL, "condition", "sample", "copy")
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    #    )                   

glbInpMerge <- NULL #: default
#     list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated

glb_is_separate_newobs_dataset <- TRUE    # or TRUE
    glb_split_entity_newobs_datasets <- TRUE  # FALSE not supported - use "copy" for glbObsNewFile$splitSpecs$method # select from c(FALSE, TRUE)

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "label"

# for classification, the response variable has to be a factor
glb_rsp_var <- "label.fctr" # or "left_eye_center_x.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
    function(raw) {
    #     return(raw ^ 0.5)
    #     return(log(raw))
    #     return(log(1 + raw))
    #     return(log10(raw)) 
    #     return(exp(-raw / 2))
    #     ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
    #     as.factor(paste0("B", raw))
        as.factor(gsub(" ", "\\.", raw))    
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, names(table(glbObsAll[, glb_rsp_var_raw])))) 

glb_map_rsp_var_to_raw <- #NULL 
    function(var) {
    #     return(var ^ 2.0)
    #     return(exp(var))
    #     return(10 ^ var) 
    #     return(-log(var) * 2)
    #     as.numeric(var)
    #     gsub("\\.", " ", levels(var)[as.numeric(var)])
        levels(var)[as.numeric(var)]
    #     c("<=50K", " >50K")[as.numeric(var)]
    #     c(FALSE, TRUE)[as.numeric(var)]
    }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "ImageId.x.y" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "patch.cor.cut.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
# glbFeatsExcludeLcl <- c(NULL
# #   Required outputs
#     ,"left_eye_center_x",         "left_eye_center_y"        
#     ,"right_eye_center_x",        "right_eye_center_y"
#     ,"left_eye_inner_corner_x",   "left_eye_inner_corner_y"  
#     ,"left_eye_outer_corner_x",   "left_eye_outer_corner_y"  
#     ,"right_eye_inner_corner_x",  "right_eye_inner_corner_y" 
#     ,"right_eye_outer_corner_x",  "right_eye_outer_corner_y" 
#     ,"left_eyebrow_inner_end_x",  "left_eyebrow_inner_end_y" 
#     ,"left_eyebrow_outer_end_x",  "left_eyebrow_outer_end_y" 
#     ,"right_eyebrow_inner_end_x", "right_eyebrow_inner_end_y"
#     ,"right_eyebrow_outer_end_x", "right_eyebrow_outer_end_y"
#     ,"nose_tip_x",                "nose_tip_y"               
#     ,"mouth_left_corner_x",       "mouth_left_corner_y"      
#     ,"mouth_right_corner_x",      "mouth_right_corner_y"     
#     ,"mouth_center_top_lip_x",    "mouth_center_top_lip_y"   
#     ,"mouth_center_bottom_lip_x", "mouth_center_bottom_lip_y"
#                     ) 
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & work each one in
#     ,setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw)
#     ,"Image.pxl.1.dgt.1"
    ,"ImageId","left_eye_center_x.int","left_eye_center_y.int","x","y","patch.cor.cut.fctr"
    # ,"patch.cor"
                    ) 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

glb_assign_pairs_lst <- NULL; 
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
#                                            to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
glbFeatsDerive[["ImageId.x.y"]] <- list(
    mapfn = function(ImageId, x, y) { 
                return(paste(ImageId, sprintf("%02d", x), sprintf("%02d", y), sep = "#")) 
            } 
  , args = c("ImageId", "x", "y"))
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(.rnorm) { return(1:length(.rnorm)) }       
    , args = c(".rnorm"))    

# glbFeatsDerive[["ImageId"]] <- list(
#     mapfn = function(.src, .pos) { 
#         # return(paste(.src, sprintf("%04d", .pos), sep = "#")) 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#"))         
#     }       
#     , args = c(".src", ".pos")) 
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

# glbFeatsDerive[["Image.pxl.1.dgt.1"]] <- list(
# #     mapfn = function(Image) { return(cut(as.integer(sapply(Image, function(img) strsplit(img, " ")[[1]][1])),
# #                                          breaks = 5)) }       
#     mapfn = function(Image) { return(substr(Image, 1, 1)) }       
#     , args = c("Image"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
glbFeatsDerive[["patch.cor.cut.fctr"]] <- list(
    mapfn = function(patch.cor) { return(cut(patch.cor, breaks = c(-1, 0, 0.5, 0.7, 1))) } 
  , args = c("patch.cor"))
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

glb_derive_vars <- names(glbFeatsDerive)

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE, 
#       last.ctg = TRUE, poly.ctg = TRUE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(Image = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
    # is.na(.rstudent)
    # is.na(.dffits)
    # .hatvalues >= 0.99        
    # -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsFitOutliers[["All.X"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
    # ,"Train#1456#72#28"
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
                                    )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")  # non-NULL vector is mandatory
# glbMdlFamilies[["All.X"]] <- c("glmnet")  # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE
glbMdlAllowParallel[["Max.cor.Y##rcv#rpart"]] <- FALSE
glbMdlAllowParallel[["Low.cor.X##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
lclgetfltout_df <- function(obsout_df) {
    require(tidyr)
    obsout_df <- obsout_df %>%
        tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
                        sep = "#", remove = TRUE, extra = "merge")
    maxout_df <- obsout_df %>%
        dplyr::group_by(.pos) %>%
        dplyr::summarize(maxProb1 = max(Probability1))
    fltout_df <- merge(maxout_df, obsout_df, 
                       by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
                       all.x = TRUE)
    return(fltout_df)
}
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
        ,mapFn = function(obsout_df) {
                    #sav_obsout_df <- obsout_df; all.equal(sav_obsout_df, obsout_df)
                    fltout_df <- lclgetfltout_df(obsout_df) %>%
                        dplyr::mutate(ImageId = as.integer(.pos)) %>%
                        dplyr::select(ImageId, left_eye_center_x = x, left_eye_center_y = y) 
                    smpout_df <- read.csv('data/IdLookupTable.csv')
                    curout_df <- fltout_df %>% 
                                    tidyr::gather(key = FeatureName, value = Location, -ImageId) %>%
                                    merge(smpout_df[, -4], all.y = TRUE, sort = FALSE) %>%
                                    dplyr::select(matches("(RowId|Location)")) %>%
                                    dplyr::filter(!is.na(Location))
                    prvout_df <- read.csv('Faces_patch_out.csv')
                    thsout_df <- rbind(curout_df, subset(prvout_df, !(RowId %in% curout_df$RowId)))
                    return(thsout_df <- orderBy(~RowId, thsout_df[, c("RowId", "Location")]))
                 }
                  )
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    glbObsOut$vars[["Probability1"]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob]" 
} else {
    glbObsOut$vars[[glbFeatsId]] <- 
        "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]"
    for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
        glbObsOut$vars[[outVar]] <- 
            paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Faces_patch_clssfr_cor_")
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- "predict.data.new" #"extract.features.end" #NULL #default: script will save envir at end of this chunk 
#mysavChunk(glbOut$pfx, glbChunks[["last"]])

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid


glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, "import.data")
##         label step_major step_minor label_minor   bgn end elapsed
## 1 import.data          1          0           0 8.439  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/Faces_patch_mean_Train.csv..."
## [1] "dimensions of data in ./data/Faces_patch_mean_Train.csv: 176,225 rows x 7 cols"
##      ImageId left_eye_center_x.int left_eye_center_y.int  x  y patch.cor
## 1 Train#0001                    66                    39 64 37 0.6483069
## 2 Train#0001                    66                    39 65 37 0.6846523
## 3 Train#0001                    66                    39 66 37 0.7029097
## 4 Train#0001                    66                    39 67 37 0.6859045
## 5 Train#0001                    66                    39 68 37 0.6399593
## 6 Train#0001                    66                    39 64 38 0.6780982
##   label
## 1 .none
## 2 .none
## 3 .none
## 4 .none
## 5 .none
## 6 .none
##           ImageId left_eye_center_x.int left_eye_center_y.int  x  y
## 6086   Train#0244                    63                    38 61 38
## 26852  Train#1075                    65                    37 64 35
## 89739  Train#3590                    68                    35 69 35
## 128240 Train#5130                    67                    37 69 37
## 129643 Train#5186                    66                    36 66 37
## 174415 Train#6977                    69                    40 71 40
##        patch.cor label
## 6086   0.4550411 .none
## 26852  0.3536069 .none
## 89739  0.6894832 .none
## 128240 0.7725618 .none
## 129643 0.6553070 .none
## 174415 0.5707231 .none
##           ImageId left_eye_center_x.int left_eye_center_y.int  x  y
## 176220 Train#7049                    66                    43 68 44
## 176221 Train#7049                    66                    43 64 45
## 176222 Train#7049                    66                    43 65 45
## 176223 Train#7049                    66                    43 66 45
## 176224 Train#7049                    66                    43 67 45
## 176225 Train#7049                    66                    43 68 45
##        patch.cor label
## 176220 0.4495034 .none
## 176221 0.3192240 .none
## 176222 0.3407551 .none
## 176223 0.3562050 .none
## 176224 0.3603249 .none
## 176225 0.3655180 .none
## 'data.frame':    176225 obs. of  7 variables:
##  $ ImageId              : chr  "Train#0001" "Train#0001" "Train#0001" "Train#0001" ...
##  $ left_eye_center_x.int: int  66 66 66 66 66 66 66 66 66 66 ...
##  $ left_eye_center_y.int: int  39 39 39 39 39 39 39 39 39 39 ...
##  $ x                    : int  64 65 66 67 68 64 65 66 67 68 ...
##  $ y                    : int  37 37 37 37 37 38 38 38 38 38 ...
##  $ patch.cor            : num  0.648 0.685 0.703 0.686 0.64 ...
##  $ label                : chr  ".none" ".none" ".none" ".none" ...
##  - attr(*, "comment")= chr "glbObsTrn"
## NULL
## [1] "Reading file ./data/Faces_patch_mean_Test.csv..."
## [1] "dimensions of data in ./data/Faces_patch_mean_Test.csv: 44,575 rows x 4 cols"
##     ImageId  x  y patch.cor
## 1 Test#0001 64 35 0.1017430
## 2 Test#0001 65 35 0.1198157
## 3 Test#0001 66 35 0.1376269
## 4 Test#0001 67 35 0.1351847
## 5 Test#0001 68 35 0.1119015
## 6 Test#0001 64 36 0.2769096
##         ImageId  x  y patch.cor
## 51    Test#0003 64 35 0.6081209
## 7977  Test#0320 65 35 0.3214708
## 17301 Test#0693 64 35 0.3450758
## 17438 Test#0698 66 37 0.6659575
## 17940 Test#0718 68 37 0.5420053
## 20615 Test#0825 68 37 0.4828386
##         ImageId  x  y patch.cor
## 44570 Test#1783 68 38 0.7325438
## 44571 Test#1783 64 39 0.7587660
## 44572 Test#1783 65 39 0.7587045
## 44573 Test#1783 66 39 0.7638962
## 44574 Test#1783 67 39 0.7673279
## 44575 Test#1783 68 39 0.7623284
## 'data.frame':    44575 obs. of  4 variables:
##  $ ImageId  : chr  "Test#0001" "Test#0001" "Test#0001" "Test#0001" ...
##  $ x        : int  64 65 66 67 68 64 65 66 67 68 ...
##  $ y        : int  35 35 35 35 35 36 36 36 36 36 ...
##  $ patch.cor: num  0.102 0.12 0.138 0.135 0.112 ...
##  - attr(*, "comment")= chr "glbObsNew"
## NULL
## [1] "Creating new feature: ImageId.x.y..."
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: patch.cor.cut.fctr..."
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##             label  .src     .n
## 1           .none Train 169186
## 2            <NA>  Test  44575
## 3 left_eye_center Train   7039
##             label  .src     .n
## 1           .none Train 169186
## 2            <NA>  Test  44575
## 3 left_eye_center Train   7039


##    .src     .n
## 1 Train 176225
## 2  Test  44575
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor    bgn   end elapsed
## 1  import.data          1          0           0  8.439 36.31  27.871
## 2 inspect.data          2          0           0 36.311    NA      NA

Step 2.0: inspect data

## Warning: Removed 44575 rows containing non-finite values (stat_count).
## Loading required package: reshape2


##       label..none label.left_eye_center label.NA
## Test           NA                    NA    44575
## Train      169186                  7039       NA
##       label..none label.left_eye_center label.NA
## Test           NA                    NA        1
## Train   0.9600567            0.03994325       NA
## [1] "numeric data missing in glbObsAll: "
## left_eye_center_x.int left_eye_center_y.int 
##                 44825                 44825 
## [1] "numeric data w/ 0s in glbObsAll: "
##         y patch.cor 
##         5       145 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##     ImageId       label ImageId.x.y 
##           0          NA           0
##             label      label.fctr     .n
## 1           .none           .none 169186
## 2            <NA>            <NA>  44575
## 3 left_eye_center left_eye_center   7039
## Warning: Removed 1 rows containing missing values (position_stack).


##       label.fctr..none label.fctr.left_eye_center label.fctr.NA
## Test                NA                         NA         44575
## Train           169186                       7039            NA
##       label.fctr..none label.fctr.left_eye_center label.fctr.NA
## Test                NA                         NA             1
## Train        0.9600567                 0.03994325            NA

!

##          label step_major step_minor label_minor    bgn    end elapsed
## 2 inspect.data          2          0           0 36.311 49.457  13.146
## 3   scrub.data          2          1           1 49.458     NA      NA

Step 2.1: scrub data

## [1] "numeric data missing in glbObsAll: "
## left_eye_center_x.int left_eye_center_y.int            label.fctr 
##                 44825                 44825                 44575 
## [1] "numeric data w/ 0s in glbObsAll: "
##         y patch.cor 
##         5       145 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##     ImageId       label ImageId.x.y 
##           0          NA           0
##            label step_major step_minor label_minor    bgn    end elapsed
## 3     scrub.data          2          1           1 49.458 51.347   1.889
## 4 transform.data          2          2           2 51.348     NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor    bgn    end elapsed
## 4   transform.data          2          2           2 51.348 51.389   0.042
## 5 extract.features          3          0           0 51.390     NA      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor    bgn   end
## 5          extract.features          3          0           0 51.390 51.41
## 6 extract.features.datetime          3          1           1 51.411    NA
##   elapsed
## 5    0.02
## 6      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor    bgn
## 1 extract.features.datetime.bgn          1          0           0 51.439
##   end elapsed
## 1  NA      NA
##                       label step_major step_minor label_minor    bgn
## 6 extract.features.datetime          3          1           1 51.411
## 7    extract.features.image          3          2           2 51.450
##      end elapsed
## 6 51.449   0.038
## 7     NA      NA

Step 3.2: extract features image

##                        label step_major step_minor label_minor    bgn end
## 1 extract.features.image.bgn          1          0           0 51.484  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor    bgn
## 1 extract.features.image.bgn          1          0           0 51.484
## 2 extract.features.image.end          2          0           0 51.494
##      end elapsed
## 1 51.494    0.01
## 2     NA      NA
##                        label step_major step_minor label_minor    bgn
## 1 extract.features.image.bgn          1          0           0 51.484
## 2 extract.features.image.end          2          0           0 51.494
##      end elapsed
## 1 51.494    0.01
## 2     NA      NA
##                    label step_major step_minor label_minor    bgn    end
## 7 extract.features.image          3          2           2 51.450 51.504
## 8 extract.features.price          3          3           3 51.505     NA
##   elapsed
## 7   0.055
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor    bgn end
## 1 extract.features.price.bgn          1          0           0 51.532  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor    bgn    end
## 8 extract.features.price          3          3           3 51.505 51.542
## 9  extract.features.text          3          4           4 51.542     NA
##   elapsed
## 8   0.037
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor    bgn end
## 1 extract.features.text.bgn          1          0           0 51.589  NA
##   elapsed
## 1      NA
##                      label step_major step_minor label_minor    bgn    end
## 9    extract.features.text          3          4           4 51.542 51.599
## 10 extract.features.string          3          5           5 51.599     NA
##    elapsed
## 9    0.057
## 10      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor    bgn end
## 1 extract.features.string.bgn          1          0           0 51.632  NA
##   elapsed
## 1      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor    bgn    end elapsed
## 1           0 51.632 51.642    0.01
## 2           0 51.642     NA      NA
##       ImageId         label          .src   ImageId.x.y 
##     "ImageId"       "label"        ".src" "ImageId.x.y"
##                      label step_major step_minor label_minor    bgn    end
## 10 extract.features.string          3          5           5 51.599 51.656
## 11    extract.features.end          3          6           6 51.657     NA
##    elapsed
## 10   0.057
## 11      NA

Step 3.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0


##                   label step_major step_minor label_minor    bgn    end
## 11 extract.features.end          3          6           6 51.657 52.586
## 12  manage.missing.data          4          0           0 52.587     NA
##    elapsed
## 11   0.929
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in glbObsAll: "
## left_eye_center_x.int left_eye_center_y.int            label.fctr 
##                 44825                 44825                 44575 
## [1] "numeric data w/ 0s in glbObsAll: "
##         y patch.cor 
##         5       145 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##     ImageId       label ImageId.x.y 
##           0          NA           0
## [1] "numeric data missing in glbObsAll: "
## left_eye_center_x.int left_eye_center_y.int            label.fctr 
##                 44825                 44825                 44575 
## [1] "numeric data w/ 0s in glbObsAll: "
##         y patch.cor 
##         5       145 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##     ImageId       label ImageId.x.y 
##           0          NA           0
##                  label step_major step_minor label_minor    bgn    end
## 12 manage.missing.data          4          0           0 52.587 53.269
## 13        cluster.data          5          0           0 53.270     NA
##    elapsed
## 12   0.682
## 13      NA

Step 5.0: cluster data

##                      label step_major step_minor label_minor    bgn    end
## 13            cluster.data          5          0           0 53.270 53.485
## 14 partition.data.training          6          0           0 53.486     NA
##    elapsed
## 13   0.215
## 14      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 1.84 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 1.84 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following objects are masked from 'package:survival':
## 
##     cluster, strata
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 5.85 secs"
##     label..none label.left_eye_center label.NA
##              NA                    NA    44575
## Fit      123158                  5286       NA
## OOB       46028                  1753       NA
##     label..none label.left_eye_center label.NA
##              NA                    NA        1
## Fit   0.9588459            0.04115412       NA
## OOB   0.9633118            0.03668822       NA
##   patch.cor.cut.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 2            (0,0.5]  45058  25583  23383     0.35079879     0.53542203
## 3          (0.5,0.7]  58107  15633  14211     0.45239170     0.32718026
## 4            (0.7,1]  23791   5078   4616     0.18522469     0.10627655
## 1             (-1,0]   1488   1487   2365     0.01158482     0.03112116
##   .freqRatio.Tst
## 2     0.52457656
## 3     0.31881099
## 4     0.10355580
## 1     0.05305665
## [1] "glbObsAll: "
## [1] 220800     14
## [1] "glbObsTrn: "
## [1] 176225     14
## [1] "glbObsFit: "
## [1] 128444     13
## [1] "glbObsOOB: "
## [1] 47781    13
## [1] "glbObsNew: "
## [1] 44575    13
## [1] "partition.data.training chunk: teardown: elapsed: 9.37 secs"
##                      label step_major step_minor label_minor    bgn    end
## 14 partition.data.training          6          0           0 53.486 62.917
## 15         select.features          7          0           0 62.917     NA
##    elapsed
## 14   9.431
## 15      NA

Step 7.0: select features

##                               cor.y exclude.as.feat    cor.y.abs
## patch.cor.cut.fctr     7.485624e-02               1 7.485624e-02
## patch.cor              7.246562e-02               0 7.246562e-02
## .pos                   2.125396e-04               0 2.125396e-04
## y                      1.246684e-05               1 1.246684e-05
## left_eye_center_x.int  5.531954e-22               1 5.531954e-22
## left_eye_center_y.int -2.301569e-21               1 2.301569e-21
## x                     -1.120225e-05               1 1.120225e-05
## .rnorm                -6.239420e-04               0 6.239420e-04
##                       cor.high.X freqRatio percentUnique zeroVar   nzv
## patch.cor.cut.fctr            NA  1.043870  2.269826e-03   FALSE FALSE
## patch.cor                     NA 24.166667  9.308384e+01   FALSE FALSE
## .pos                          NA  1.000000  1.000000e+02   FALSE FALSE
## y                             NA  1.061821  4.539651e-02   FALSE FALSE
## left_eye_center_x.int         NA  1.090377  2.837282e-02   FALSE FALSE
## left_eye_center_y.int         NA  1.028810  2.383317e-02   FALSE FALSE
## x                             NA  1.059822  3.915449e-02   FALSE FALSE
## .rnorm                        NA  1.000000  9.940985e+01   FALSE FALSE
##                       is.cor.y.abs.low
## patch.cor.cut.fctr               FALSE
## patch.cor                        FALSE
## .pos                              TRUE
## y                                 TRUE
## left_eye_center_x.int             TRUE
## left_eye_center_y.int             TRUE
## x                                 TRUE
## .rnorm                           FALSE
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).


## [1] cor.y            exclude.as.feat  cor.y.abs        cor.high.X      
## [5] freqRatio        percentUnique    zeroVar          nzv             
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)


## [1] "numeric data missing in glbObsAll: "
## left_eye_center_x.int left_eye_center_y.int            label.fctr 
##                 44825                 44825                 44575 
## [1] "numeric data w/ 0s in glbObsAll: "
##         y patch.cor 
##         5       145 
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##     ImageId       label ImageId.x.y        .lcn 
##           0          NA           0       44575
## [1] "glb_feats_df:"
## [1]  8 12
##                    id exclude.as.feat rsp_var
## label.fctr label.fctr            TRUE    TRUE
##                    id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## label.fctr label.fctr    NA            TRUE        NA         NA        NA
##            percentUnique zeroVar nzv is.cor.y.abs.low interaction.feat
## label.fctr            NA      NA  NA               NA               NA
##            shapiro.test.p.value rsp_var_raw id_var rsp_var
## label.fctr                   NA          NA     NA    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor    bgn    end elapsed
## 15 select.features          7          0           0 62.917 76.532  13.615
## 16      fit.models          8          0           0 76.532     NA      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 77.023  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
c("id.prefix", "method", "type",
  # trainControl params
  "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
  # train params
  "metric", "metric.maximize", "tune.df")
##  [1] "id.prefix"       "method"          "type"           
##  [4] "preProc.method"  "cv.n.folds"      "cv.n.repeats"   
##  [7] "summary.fn"      "metric"          "metric.maximize"
## [10] "tune.df"
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indep_vars_vctr=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indep_vars = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
#     ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#         id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
#         train.method = "myrandom_classfr")),
#                         indep_vars = ".rnorm", rsp_var = glb_rsp_var,
#                         fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor    bgn    end
## 1 fit.models_0_bgn          1          0         setup 77.023 77.059
## 2 fit.models_0_MFO          1          1 myMFO_classfr 77.060     NA
##   elapsed
## 1   0.036
## 2      NA
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] .none           left_eye_center
## Levels: .none left_eye_center
## [1] "unique.prob:"
## y
##           .none left_eye_center 
##      0.95884588      0.04115412 
## [1] "MFO.val:"
## [1] ".none"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##       .none left_eye_center
## 1 0.9588459      0.04115412
## 2 0.9588459      0.04115412
## 3 0.9588459      0.04115412
## 4 0.9588459      0.04115412
## 5 0.9588459      0.04115412
## 6 0.9588459      0.04115412


##                  Prediction
## Reference          .none left_eye_center
##   .none                0          123158
##   left_eye_center      0            5286
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.04115412     0.00000000     0.04007424     0.04225478     0.95884588 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##       .none left_eye_center
## 1 0.9588459      0.04115412
## 2 0.9588459      0.04115412
## 3 0.9588459      0.04115412
## 4 0.9588459      0.04115412
## 5 0.9588459      0.04115412
## 6 0.9588459      0.04115412


##                  Prediction
## Reference         .none left_eye_center
##   .none               0           46028
##   left_eye_center     0            1753
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.03668822     0.00000000     0.03502031     0.03841273     0.96331178 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.555
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.018             0.5            1            0
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                      0      0.07905481       0.04115412
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1            0.04007424            0.04225478             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            1            0             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                      0      0.07077967       0.03668822
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1            0.03502031            0.03841273             0
##                 label step_major step_minor      label_minor    bgn    end
## 2    fit.models_0_MFO          1          1    myMFO_classfr 77.060 92.024
## 3 fit.models_0_Random          1          2 myrandom_classfr 92.025     NA
##   elapsed
## 2  14.964
## 3      NA
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##      bgn    end elapsed
## 3 92.025 92.036   0.011
## 4 92.037     NA      NA
# ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
#     id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
#     train.method="glmnet")),
#                     indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indep_vars: patch.cor,.rnorm"
## Loading required package: rpart
## + Fold1.Rep1: cp=2.365e-05 
## - Fold1.Rep1: cp=2.365e-05 
## + Fold2.Rep1: cp=2.365e-05 
## - Fold2.Rep1: cp=2.365e-05 
## + Fold3.Rep1: cp=2.365e-05 
## - Fold3.Rep1: cp=2.365e-05 
## + Fold1.Rep2: cp=2.365e-05 
## - Fold1.Rep2: cp=2.365e-05 
## + Fold2.Rep2: cp=2.365e-05 
## - Fold2.Rep2: cp=2.365e-05 
## + Fold3.Rep2: cp=2.365e-05 
## - Fold3.Rep2: cp=2.365e-05 
## + Fold1.Rep3: cp=2.365e-05 
## - Fold1.Rep3: cp=2.365e-05 
## + Fold2.Rep3: cp=2.365e-05 
## - Fold2.Rep3: cp=2.365e-05 
## + Fold3.Rep3: cp=2.365e-05 
## - Fold3.Rep3: cp=2.365e-05 
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 5.82e-05 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Loading required package: rpart.plot

!

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 128444 
## 
##             CP nsplit rel error
## 1 5.820891e-05      0         1
## 
## Node number 1: 128444 observations
##   predicted class=.none  expected loss=0.04115412  P(node) =1
##     class counts: 123158  5286
##    probabilities: 0.959 0.041 
## 
## n= 128444 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 128444 5286 .none (0.95884588 0.04115412) *


##                  Prediction
## Reference          .none left_eye_center
##   .none                0          123158
##   left_eye_center      0            5286
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.04115412     0.00000000     0.04007424     0.04225478     0.95884588 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000


##                  Prediction
## Reference         .none left_eye_center
##   .none               0           46028
##   left_eye_center     0            1753
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.03668822     0.00000000     0.03502031     0.03841273     0.96331178 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
##                     id            feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart patch.cor,.rnorm               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                     31.161                 1.148             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0             0.5                      0
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1      0.07905481        0.9587213            0.04007424
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1            0.04225478 -0.0001323986             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0             0.5                      0      0.07077967
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1       0.03668822            0.03502031            0.03841273
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0       0.0001820465    0.0003480858
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjust_interaction_feats(indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indep_vars = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Low.cor.X
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                         paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
#                                      label.minor = "glmnet")
# indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv & 
#                               (exclude.as.feat != 1))[, "id"]  
# indep_vars <- myadjust_interaction_feats(indep_vars)
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#             id.prefix = "Low.cor.X", 
#             type = glb_model_type, 
#             tune.df = glbMdlTuneParams,        
#             trainControl.method = "repeatedcv",
#             trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
#             trainControl.classProbs = glb_is_classification,
#             trainControl.summaryFunction = glbMdlMetricSummaryFn,
#             trainControl.allowParallel = glbMdlAllowParallel,
#             train.metric = glbMdlMetricSummary, 
#             train.maximize = glbMdlMetricMaximize,    
#             train.method = "glmnet")),
#         indep_vars = indep_vars, rsp_var = glb_rsp_var, 
#         fit_df = glbObsFit, OOB_df = glbObsOOB)

fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                            label step_major step_minor label_minor     bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet  92.037
## 5               fit.models_0_end          1          4    teardown 138.411
##       end elapsed
## 4 138.411  46.374
## 5      NA      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          8          0           0  76.532 138.424  61.893
## 17 fit.models          8          1           1 138.425      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 139.436  NA      NA
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indep_vars <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjust_interaction_feats(myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
            indep_vars <- setdiff(indep_vars, topindep_var)
            if (length(interact_vars) > 0) {
                indep_vars <- 
                    setdiff(indep_vars, myextract_actual_feats(interact_vars))
                indep_vars <- c(indep_vars, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indep_vars <- union(indep_vars, topindep_var)
        }
    }
    
    if (is.null(indep_vars))
        indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
        indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indep_vars))
        indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
    
    if ((length(indep_vars) == 1) && (grepl("^%<d-%", indep_vars))) {    
        indep_vars <- 
            eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
    }    

    indep_vars <- myadjust_interaction_feats(indep_vars)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indep_vars <- setdiff(indep_vars, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indep_vars = indep_vars, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indep_vars]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indep_vars]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 139.436 139.447
## 2 fit.models_1_All.X          1          1       setup 139.448      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 139.448 139.455
## 3 fit.models_1_All.X          1          2      glmnet 139.456      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indep_vars: patch.cor,.pos,.rnorm"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-2
## + Fold1.Rep1: alpha=0.100, lambda=0.007631 
## - Fold1.Rep1: alpha=0.100, lambda=0.007631 
## + Fold1.Rep1: alpha=0.325, lambda=0.007631 
## - Fold1.Rep1: alpha=0.325, lambda=0.007631 
## + Fold1.Rep1: alpha=0.550, lambda=0.007631 
## - Fold1.Rep1: alpha=0.550, lambda=0.007631 
## + Fold1.Rep1: alpha=0.775, lambda=0.007631 
## - Fold1.Rep1: alpha=0.775, lambda=0.007631 
## + Fold1.Rep1: alpha=1.000, lambda=0.007631 
## - Fold1.Rep1: alpha=1.000, lambda=0.007631 
## + Fold2.Rep1: alpha=0.100, lambda=0.007631 
## - Fold2.Rep1: alpha=0.100, lambda=0.007631 
## + Fold2.Rep1: alpha=0.325, lambda=0.007631 
## - Fold2.Rep1: alpha=0.325, lambda=0.007631 
## + Fold2.Rep1: alpha=0.550, lambda=0.007631 
## - Fold2.Rep1: alpha=0.550, lambda=0.007631 
## + Fold2.Rep1: alpha=0.775, lambda=0.007631 
## - Fold2.Rep1: alpha=0.775, lambda=0.007631 
## + Fold2.Rep1: alpha=1.000, lambda=0.007631 
## - Fold2.Rep1: alpha=1.000, lambda=0.007631 
## + Fold3.Rep1: alpha=0.100, lambda=0.007631 
## - Fold3.Rep1: alpha=0.100, lambda=0.007631 
## + Fold3.Rep1: alpha=0.325, lambda=0.007631 
## - Fold3.Rep1: alpha=0.325, lambda=0.007631 
## + Fold3.Rep1: alpha=0.550, lambda=0.007631 
## - Fold3.Rep1: alpha=0.550, lambda=0.007631 
## + Fold3.Rep1: alpha=0.775, lambda=0.007631 
## - Fold3.Rep1: alpha=0.775, lambda=0.007631 
## + Fold3.Rep1: alpha=1.000, lambda=0.007631 
## - Fold3.Rep1: alpha=1.000, lambda=0.007631 
## + Fold1.Rep2: alpha=0.100, lambda=0.007631 
## - Fold1.Rep2: alpha=0.100, lambda=0.007631 
## + Fold1.Rep2: alpha=0.325, lambda=0.007631 
## - Fold1.Rep2: alpha=0.325, lambda=0.007631 
## + Fold1.Rep2: alpha=0.550, lambda=0.007631 
## - Fold1.Rep2: alpha=0.550, lambda=0.007631 
## + Fold1.Rep2: alpha=0.775, lambda=0.007631 
## - Fold1.Rep2: alpha=0.775, lambda=0.007631 
## + Fold1.Rep2: alpha=1.000, lambda=0.007631 
## - Fold1.Rep2: alpha=1.000, lambda=0.007631 
## + Fold2.Rep2: alpha=0.100, lambda=0.007631 
## - Fold2.Rep2: alpha=0.100, lambda=0.007631 
## + Fold2.Rep2: alpha=0.325, lambda=0.007631 
## - Fold2.Rep2: alpha=0.325, lambda=0.007631 
## + Fold2.Rep2: alpha=0.550, lambda=0.007631 
## - Fold2.Rep2: alpha=0.550, lambda=0.007631 
## + Fold2.Rep2: alpha=0.775, lambda=0.007631 
## - Fold2.Rep2: alpha=0.775, lambda=0.007631 
## + Fold2.Rep2: alpha=1.000, lambda=0.007631 
## - Fold2.Rep2: alpha=1.000, lambda=0.007631 
## + Fold3.Rep2: alpha=0.100, lambda=0.007631 
## - Fold3.Rep2: alpha=0.100, lambda=0.007631 
## + Fold3.Rep2: alpha=0.325, lambda=0.007631 
## - Fold3.Rep2: alpha=0.325, lambda=0.007631 
## + Fold3.Rep2: alpha=0.550, lambda=0.007631 
## - Fold3.Rep2: alpha=0.550, lambda=0.007631 
## + Fold3.Rep2: alpha=0.775, lambda=0.007631 
## - Fold3.Rep2: alpha=0.775, lambda=0.007631 
## + Fold3.Rep2: alpha=1.000, lambda=0.007631 
## - Fold3.Rep2: alpha=1.000, lambda=0.007631 
## + Fold1.Rep3: alpha=0.100, lambda=0.007631 
## - Fold1.Rep3: alpha=0.100, lambda=0.007631 
## + Fold1.Rep3: alpha=0.325, lambda=0.007631 
## - Fold1.Rep3: alpha=0.325, lambda=0.007631 
## + Fold1.Rep3: alpha=0.550, lambda=0.007631 
## - Fold1.Rep3: alpha=0.550, lambda=0.007631 
## + Fold1.Rep3: alpha=0.775, lambda=0.007631 
## - Fold1.Rep3: alpha=0.775, lambda=0.007631 
## + Fold1.Rep3: alpha=1.000, lambda=0.007631 
## - Fold1.Rep3: alpha=1.000, lambda=0.007631 
## + Fold2.Rep3: alpha=0.100, lambda=0.007631 
## - Fold2.Rep3: alpha=0.100, lambda=0.007631 
## + Fold2.Rep3: alpha=0.325, lambda=0.007631 
## - Fold2.Rep3: alpha=0.325, lambda=0.007631 
## + Fold2.Rep3: alpha=0.550, lambda=0.007631 
## - Fold2.Rep3: alpha=0.550, lambda=0.007631 
## + Fold2.Rep3: alpha=0.775, lambda=0.007631 
## - Fold2.Rep3: alpha=0.775, lambda=0.007631 
## + Fold2.Rep3: alpha=1.000, lambda=0.007631 
## - Fold2.Rep3: alpha=1.000, lambda=0.007631 
## + Fold3.Rep3: alpha=0.100, lambda=0.007631 
## - Fold3.Rep3: alpha=0.100, lambda=0.007631 
## + Fold3.Rep3: alpha=0.325, lambda=0.007631 
## - Fold3.Rep3: alpha=0.325, lambda=0.007631 
## + Fold3.Rep3: alpha=0.550, lambda=0.007631 
## - Fold3.Rep3: alpha=0.550, lambda=0.007631 
## + Fold3.Rep3: alpha=0.775, lambda=0.007631 
## - Fold3.Rep3: alpha=0.775, lambda=0.007631 
## + Fold3.Rep3: alpha=1.000, lambda=0.007631 
## - Fold3.Rep3: alpha=1.000, lambda=0.007631 
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00763 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

!

##             Length Class      Mode     
## a0           56    -none-     numeric  
## beta        168    dgCMatrix  S4       
## df           56    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       56    -none-     numeric  
## dev.ratio    56    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        3    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.346471e+00 -3.562886e-07  2.179778e+00 
## [1] "max lambda < lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.373634e+00 -4.046156e-07  2.232784e+00

!

##                  Prediction
## Reference          .none left_eye_center
##   .none                0          123158
##   left_eye_center      0            5286
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.04115412     0.00000000     0.04007424     0.04225478     0.95884588 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000

!

##                  Prediction
## Reference         .none left_eye_center
##   .none               0           46028
##   left_eye_center     0            1753
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.03668822     0.00000000     0.03502031     0.03841273     0.96331178 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
##                  id                 feats max.nTuningRuns
## 1 All.X##rcv#glmnet patch.cor,.pos,.rnorm              20
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                     77.851                 1.196             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0       0.6423301                      0
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1      0.07905481        0.9588459            0.04007424
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1            0.04225478             0             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0       0.5404133                      0      0.07077967
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1       0.03668822            0.03502031            0.03841273
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0       4.806115e-07               0
##                label step_major step_minor label_minor     bgn    end
## 3 fit.models_1_All.X          1          2      glmnet 139.456 328.56
## 4 fit.models_1_All.X          1          3         glm 328.562     NA
##   elapsed
## 3 189.104
## 4      NA
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indep_vars: patch.cor,.pos,.rnorm"
## + Fold1.Rep1: parameter=none 
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none 
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none 
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none 
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none 
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none 
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none 
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none 
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none 
## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set

!!!

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4853  -0.3285  -0.2799  -0.2230   3.6586  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.757e+00  6.159e-02 -77.230  < 2e-16 ***
## .pos        -1.038e-06  2.804e-07  -3.702 0.000214 ***
## .rnorm      -1.412e-02  1.418e-02  -0.996 0.319079    
## patch.cor    2.962e+00  9.277e-02  31.929  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 44081  on 128443  degrees of freedom
## Residual deviance: 42893  on 128440  degrees of freedom
## AIC: 42901
## 
## Number of Fisher Scoring iterations: 6

!

##                  Prediction
## Reference          .none left_eye_center
##   .none                0          123158
##   left_eye_center      0            5286
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.04115412     0.00000000     0.04007424     0.04225478     0.95884588 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000

!

##                  Prediction
## Reference         .none left_eye_center
##   .none               0           46028
##   left_eye_center     0            1753
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.03668822     0.00000000     0.03502031     0.03841273     0.96331178 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
##               id                 feats max.nTuningRuns
## 1 All.X##rcv#glm patch.cor,.pos,.rnorm               1
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                     13.161                 0.961             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0       0.6428006                      0
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1      0.07905481        0.9588459            0.04007424
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1            0.04225478             0             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0       0.5398062                      0      0.07077967
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1       0.03668822            0.03502031            0.03841273
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0       4.806115e-07               0
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 328.562 466.888
## 5 fit.models_1_preProc          1          4     preProc 466.889      NA
##   elapsed
## 4 138.326
## 5      NA
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indep_vars_vctr
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indep_vars_vctr=indep_vars_vctr,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indep_vars_vctr_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indep_vars_vctr_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indep_vars_vctr_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indep_vars_vctr=indep_vars_vctr,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                        id                 feats
## MFO###myMFO_classfr   MFO###myMFO_classfr                .rnorm
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart      patch.cor,.rnorm
## All.X##rcv#glmnet       All.X##rcv#glmnet patch.cor,.pos,.rnorm
## All.X##rcv#glm             All.X##rcv#glm patch.cor,.pos,.rnorm
##                      max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                0                      0.555
## Max.cor.Y##rcv#rpart               5                     31.161
## All.X##rcv#glmnet                 20                     77.851
## All.X##rcv#glm                     1                     13.161
##                      min.elapsedtime.final max.AUCpROC.fit max.Sens.fit
## MFO###myMFO_classfr                  0.018             0.5            1
## Max.cor.Y##rcv#rpart                 1.148             0.5            1
## All.X##rcv#glmnet                    1.196             0.5            1
## All.X##rcv#glm                       0.961             0.5            1
##                      max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## MFO###myMFO_classfr             0       0.5000000                      0
## Max.cor.Y##rcv#rpart            0       0.5000000                      0
## All.X##rcv#glmnet               0       0.6423301                      0
## All.X##rcv#glm                  0       0.6428006                      0
##                      max.f.score.fit max.Accuracy.fit
## MFO###myMFO_classfr       0.07905481       0.04115412
## Max.cor.Y##rcv#rpart      0.07905481       0.95872131
## All.X##rcv#glmnet         0.07905481       0.95884588
## All.X##rcv#glm            0.07905481       0.95884588
##                      max.AccuracyLower.fit max.AccuracyUpper.fit
## MFO###myMFO_classfr             0.04007424            0.04225478
## Max.cor.Y##rcv#rpart            0.04007424            0.04225478
## All.X##rcv#glmnet               0.04007424            0.04225478
## All.X##rcv#glm                  0.04007424            0.04225478
##                      max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## MFO###myMFO_classfr   0.0000000000             0.5            1
## Max.cor.Y##rcv#rpart -0.0001323986             0.5            1
## All.X##rcv#glmnet     0.0000000000             0.5            1
## All.X##rcv#glm        0.0000000000             0.5            1
##                      max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr             0       0.5000000                      0
## Max.cor.Y##rcv#rpart            0       0.5000000                      0
## All.X##rcv#glmnet               0       0.5404133                      0
## All.X##rcv#glm                  0       0.5398062                      0
##                      max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr       0.07077967       0.03668822
## Max.cor.Y##rcv#rpart      0.07077967       0.03668822
## All.X##rcv#glmnet         0.07077967       0.03668822
## All.X##rcv#glm            0.07077967       0.03668822
##                      max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO###myMFO_classfr             0.03502031            0.03841273
## Max.cor.Y##rcv#rpart            0.03502031            0.03841273
## All.X##rcv#glmnet               0.03502031            0.03841273
## All.X##rcv#glm                  0.03502031            0.03841273
##                      max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr              0                 NA              NA
## Max.cor.Y##rcv#rpart             0       1.820465e-04    0.0003480858
## All.X##rcv#glmnet                0       4.806115e-07    0.0000000000
## All.X##rcv#glm                   0       4.806115e-07    0.0000000000
rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 466.889 466.965
## 6     fit.models_1_end          1          5    teardown 466.966      NA
##   elapsed
## 5   0.076
## 6      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 17 fit.models          8          1           1 138.425 466.976 328.551
## 18 fit.models          8          2           2 466.977      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 498.021  NA      NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                        id                 feats
## MFO###myMFO_classfr   MFO###myMFO_classfr                .rnorm
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart      patch.cor,.rnorm
## All.X##rcv#glmnet       All.X##rcv#glmnet patch.cor,.pos,.rnorm
## All.X##rcv#glm             All.X##rcv#glm patch.cor,.pos,.rnorm
##                      max.nTuningRuns max.AUCpROC.fit max.Sens.fit
## MFO###myMFO_classfr                0             0.5            1
## Max.cor.Y##rcv#rpart               5             0.5            1
## All.X##rcv#glmnet                 20             0.5            1
## All.X##rcv#glm                     1             0.5            1
##                      max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## MFO###myMFO_classfr             0       0.5000000                      0
## Max.cor.Y##rcv#rpart            0       0.5000000                      0
## All.X##rcv#glmnet               0       0.6423301                      0
## All.X##rcv#glm                  0       0.6428006                      0
##                      max.f.score.fit max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr       0.07905481       0.04115412  0.0000000000
## Max.cor.Y##rcv#rpart      0.07905481       0.95872131 -0.0001323986
## All.X##rcv#glmnet         0.07905481       0.95884588  0.0000000000
## All.X##rcv#glm            0.07905481       0.95884588  0.0000000000
##                      max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr              0.5            1            0
## Max.cor.Y##rcv#rpart             0.5            1            0
## All.X##rcv#glmnet                0.5            1            0
## All.X##rcv#glm                   0.5            1            0
##                      max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr        0.5000000                      0
## Max.cor.Y##rcv#rpart       0.5000000                      0
## All.X##rcv#glmnet          0.5404133                      0
## All.X##rcv#glm             0.5398062                      0
##                      max.f.score.OOB max.Accuracy.OOB max.Kappa.OOB
## MFO###myMFO_classfr       0.07077967       0.03668822             0
## Max.cor.Y##rcv#rpart      0.07077967       0.03668822             0
## All.X##rcv#glmnet         0.07077967       0.03668822             0
## All.X##rcv#glm            0.07077967       0.03668822             0
##                      inv.elapsedtime.everything inv.elapsedtime.final
## MFO###myMFO_classfr                  1.80180180            55.5555556
## Max.cor.Y##rcv#rpart                 0.03209140             0.8710801
## All.X##rcv#glmnet                    0.01284505             0.8361204
## All.X##rcv#glm                       0.07598207             1.0405827
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Stacking not well defined when ymin != 0
## Warning: Removed 2 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Stacking not well defined when ymin != 0

## Warning: Removed 2 rows containing missing values (geom_errorbar).


dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                     id max.Accuracy.OOB max.AUCROCR.OOB max.AUCpROC.OOB
## 3    All.X##rcv#glmnet       0.03668822       0.5404133             0.5
## 4       All.X##rcv#glm       0.03668822       0.5398062             0.5
## 2 Max.cor.Y##rcv#rpart       0.03668822       0.5000000             0.5
## 1  MFO###myMFO_classfr       0.03668822       0.5000000             0.5
##   max.Accuracy.fit opt.prob.threshold.fit opt.prob.threshold.OOB
## 3       0.95884588                      0                      0
## 4       0.95884588                      0                      0
## 2       0.95872131                      0                      0
## 1       0.04115412                      0                      0
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7f8bb15599f0>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: All.X##rcv#glmnet"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indep_vars <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indep_vars <- paste(indep_vars, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indep_vars <- intersect(indep_vars, names(glbObsFit))
    
#     indep_vars <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
#     if (glb_is_classification && glb_is_binomial)
#         indep_vars <- grep("prob$", indep_vars, value=TRUE) else
#         indep_vars <- indep_vars[!grepl("err$", indep_vars)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indep_vars)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indep_vars = indep_vars, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glb_sel_mdl_id)) 
    glb_sel_mdl_id <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glb_sel_mdl_id))   
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])


##             Length Class      Mode     
## a0           56    -none-     numeric  
## beta        168    dgCMatrix  S4       
## df           56    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       56    -none-     numeric  
## dev.ratio    56    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        3    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.346471e+00 -3.562886e-07  2.179778e+00 
## [1] "max lambda < lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.373634e+00 -4.046156e-07  2.232784e+00
## [1] TRUE
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##           All.X..rcv.glmnet.imp          imp
## patch.cor          1.000000e+02 1.000000e+02
## .pos               1.778514e-05 1.778514e-05
## .rnorm             0.000000e+00 0.000000e+00
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
            prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id)                  

!!

## [1] "Min/Max Boundaries: "
##        ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 1 Train#1456#72#27      .none                       0.002555233
## 2 Train#7049#66#45      .none                       0.025422637
## 3 Train#0001#65#38      .none                       0.060472363
## 4 Train#5462#64#38      .none                       0.087649486
##   label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 1              left_eye_center                             TRUE
## 2              left_eye_center                             TRUE
## 3              left_eye_center                             TRUE
## 4              left_eye_center                             TRUE
##   label.fctr.All.X..rcv.glmnet.err.abs label.fctr.All.X..rcv.glmnet.is.acc
## 1                          0.002555233                               FALSE
## 2                          0.025422637                               FALSE
## 3                          0.060472363                               FALSE
## 4                          0.087649486                               FALSE
##   label.fctr.All.X..rcv.glmnet.accurate label.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                        0.002555233
## 2                                 FALSE                        0.025422637
## 3                                 FALSE                        0.060472363
## 4                                 FALSE                        0.087649486
##             .label
## 1 Train#1456#72#27
## 2 Train#7049#66#45
## 3 Train#0001#65#38
## 4 Train#5462#64#38
## [1] "Inaccurate: "
##        ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 1 Train#1456#72#27      .none                       0.002555233
## 2 Train#1456#73#26      .none                       0.002565044
## 3 Train#1456#72#26      .none                       0.002599612
## 4 Train#1456#70#27      .none                       0.002634658
## 5 Train#1549#76#48      .none                       0.002837853
## 6 Train#1456#70#28      .none                       0.003032908
##   label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 1              left_eye_center                             TRUE
## 2              left_eye_center                             TRUE
## 3              left_eye_center                             TRUE
## 4              left_eye_center                             TRUE
## 5              left_eye_center                             TRUE
## 6              left_eye_center                             TRUE
##   label.fctr.All.X..rcv.glmnet.err.abs label.fctr.All.X..rcv.glmnet.is.acc
## 1                          0.002555233                               FALSE
## 2                          0.002565044                               FALSE
## 3                          0.002599612                               FALSE
## 4                          0.002634658                               FALSE
## 5                          0.002837853                               FALSE
## 6                          0.003032908                               FALSE
##   label.fctr.All.X..rcv.glmnet.accurate label.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                        0.002555233
## 2                                 FALSE                        0.002565044
## 3                                 FALSE                        0.002599612
## 4                                 FALSE                        0.002634658
## 5                                 FALSE                        0.002837853
## 6                                 FALSE                        0.003032908
##            ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 10424 Train#7034#66#33      .none                        0.02438915
## 15931 Train#0401#66#39      .none                        0.02883764
## 24106 Train#0513#67#38      .none                        0.03450133
## 31769 Train#5959#66#40      .none                        0.04208510
## 33288 Train#5492#67#40      .none                        0.04374356
## 39616 Train#4380#68#38      .none                        0.05215980
##       label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 10424              left_eye_center                             TRUE
## 15931              left_eye_center                             TRUE
## 24106              left_eye_center                             TRUE
## 31769              left_eye_center                             TRUE
## 33288              left_eye_center                             TRUE
## 39616              left_eye_center                             TRUE
##       label.fctr.All.X..rcv.glmnet.err.abs
## 10424                           0.02438915
## 15931                           0.02883764
## 24106                           0.03450133
## 31769                           0.04208510
## 33288                           0.04374356
## 39616                           0.05215980
##       label.fctr.All.X..rcv.glmnet.is.acc
## 10424                               FALSE
## 15931                               FALSE
## 24106                               FALSE
## 31769                               FALSE
## 33288                               FALSE
## 39616                               FALSE
##       label.fctr.All.X..rcv.glmnet.accurate
## 10424                                 FALSE
## 15931                                 FALSE
## 24106                                 FALSE
## 31769                                 FALSE
## 33288                                 FALSE
## 39616                                 FALSE
##       label.fctr.All.X..rcv.glmnet.error
## 10424                         0.02438915
## 15931                         0.02883764
## 24106                         0.03450133
## 31769                         0.04208510
## 33288                         0.04374356
## 39616                         0.05215980
##            ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 46023 Train#0050#64#40      .none                        0.08527816
## 46024 Train#6676#62#38      .none                        0.08543264
## 46025 Train#1646#63#37      .none                        0.08566914
## 46026 Train#4052#69#36      .none                        0.08571618
## 46027 Train#5462#64#38      .none                        0.08764949
## 46028 Train#3808#68#36      .none                        0.08838562
##       label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 46023              left_eye_center                             TRUE
## 46024              left_eye_center                             TRUE
## 46025              left_eye_center                             TRUE
## 46026              left_eye_center                             TRUE
## 46027              left_eye_center                             TRUE
## 46028              left_eye_center                             TRUE
##       label.fctr.All.X..rcv.glmnet.err.abs
## 46023                           0.08527816
## 46024                           0.08543264
## 46025                           0.08566914
## 46026                           0.08571618
## 46027                           0.08764949
## 46028                           0.08838562
##       label.fctr.All.X..rcv.glmnet.is.acc
## 46023                               FALSE
## 46024                               FALSE
## 46025                               FALSE
## 46026                               FALSE
## 46027                               FALSE
## 46028                               FALSE
##       label.fctr.All.X..rcv.glmnet.accurate
## 46023                                 FALSE
## 46024                                 FALSE
## 46025                                 FALSE
## 46026                                 FALSE
## 46027                                 FALSE
## 46028                                 FALSE
##       label.fctr.All.X..rcv.glmnet.error
## 46023                         0.08527816
## 46024                         0.08543264
## 46025                         0.08566914
## 46026                         0.08571618
## 46027                         0.08764949
## 46028                         0.08838562


if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##           patch.cor.cut.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## (0.7,1]              (0.7,1]   5078  23791   4616     0.18522469
## (0.5,0.7]          (0.5,0.7]  15633  58107  14211     0.45239170
## (0,0.5]              (0,0.5]  25583  45058  23383     0.35079879
## (-1,0]                (-1,0]   1487   1488   2365     0.01158482
##           .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## (0.7,1]       0.10627655     0.10355580      3009.86272       0.12651266
## (0.5,0.7]     0.32718026     0.31881099      4972.21940       0.08557006
## (0,0.5]       0.53542203     0.52457656      2031.94587       0.04509623
## (-1,0]        0.03112116     0.05305665        50.00137       0.03360307
##           .n.fit err.abs.OOB.sum err.abs.OOB.mean
## (0.7,1]    23791       493.53456       0.09719074
## (0.5,0.7]  58107      1265.33217       0.08093982
## (0,0.5]    45058      1518.01621       0.05933691
## (-1,0]      1488        50.03252       0.03364661
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##     4.778100e+04     1.284440e+05     4.457500e+04     1.000000e+00 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##     1.000000e+00     1.000000e+00     1.006403e+04     2.907820e-01 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##     1.284440e+05     3.326915e+03     2.711141e-01
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glb_sel_mdl_id), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 525.05  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 18 fit.models          8          2           2 466.977 525.091  58.114
## 19 fit.models          8          3           3 525.092      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0


glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)
##                label step_major step_minor label_minor     bgn    end
## 19        fit.models          8          3           3 525.092 544.92
## 20 fit.data.training          9          0           0 544.920     NA
##    elapsed
## 19  19.828
## 20      NA

Step 9.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glb_sel_mdl_id")
    glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indep_vars <- myadjust_interaction_feats(subset(glb_feats_df, 
                                                    !nzv & (exclude.as.feat != 1))[, "id"])
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indep_vars, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indep_vars = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
        
    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
        indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indep_vars_vctr <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glb_sel_mdl_id
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glb_sel_mdl_id)) != -1))
        ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
    }
        
    if ((length(method_vctr) == 1) || (method != "glm")) {
        glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
        glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
    }
}
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indep_vars: patch.cor,.pos,.rnorm"
## + Fold1.Rep1: alpha=0.100, lambda=0.006115 
## - Fold1.Rep1: alpha=0.100, lambda=0.006115 
## + Fold1.Rep1: alpha=0.325, lambda=0.006115 
## - Fold1.Rep1: alpha=0.325, lambda=0.006115 
## + Fold1.Rep1: alpha=0.550, lambda=0.006115 
## - Fold1.Rep1: alpha=0.550, lambda=0.006115 
## + Fold1.Rep1: alpha=0.775, lambda=0.006115 
## - Fold1.Rep1: alpha=0.775, lambda=0.006115 
## + Fold1.Rep1: alpha=1.000, lambda=0.006115 
## - Fold1.Rep1: alpha=1.000, lambda=0.006115 
## + Fold2.Rep1: alpha=0.100, lambda=0.006115 
## - Fold2.Rep1: alpha=0.100, lambda=0.006115 
## + Fold2.Rep1: alpha=0.325, lambda=0.006115 
## - Fold2.Rep1: alpha=0.325, lambda=0.006115 
## + Fold2.Rep1: alpha=0.550, lambda=0.006115 
## - Fold2.Rep1: alpha=0.550, lambda=0.006115 
## + Fold2.Rep1: alpha=0.775, lambda=0.006115 
## - Fold2.Rep1: alpha=0.775, lambda=0.006115 
## + Fold2.Rep1: alpha=1.000, lambda=0.006115 
## - Fold2.Rep1: alpha=1.000, lambda=0.006115 
## + Fold3.Rep1: alpha=0.100, lambda=0.006115 
## - Fold3.Rep1: alpha=0.100, lambda=0.006115 
## + Fold3.Rep1: alpha=0.325, lambda=0.006115 
## - Fold3.Rep1: alpha=0.325, lambda=0.006115 
## + Fold3.Rep1: alpha=0.550, lambda=0.006115 
## - Fold3.Rep1: alpha=0.550, lambda=0.006115 
## + Fold3.Rep1: alpha=0.775, lambda=0.006115 
## - Fold3.Rep1: alpha=0.775, lambda=0.006115 
## + Fold3.Rep1: alpha=1.000, lambda=0.006115 
## - Fold3.Rep1: alpha=1.000, lambda=0.006115 
## + Fold1.Rep2: alpha=0.100, lambda=0.006115 
## - Fold1.Rep2: alpha=0.100, lambda=0.006115 
## + Fold1.Rep2: alpha=0.325, lambda=0.006115 
## - Fold1.Rep2: alpha=0.325, lambda=0.006115 
## + Fold1.Rep2: alpha=0.550, lambda=0.006115 
## - Fold1.Rep2: alpha=0.550, lambda=0.006115 
## + Fold1.Rep2: alpha=0.775, lambda=0.006115 
## - Fold1.Rep2: alpha=0.775, lambda=0.006115 
## + Fold1.Rep2: alpha=1.000, lambda=0.006115 
## - Fold1.Rep2: alpha=1.000, lambda=0.006115 
## + Fold2.Rep2: alpha=0.100, lambda=0.006115 
## - Fold2.Rep2: alpha=0.100, lambda=0.006115 
## + Fold2.Rep2: alpha=0.325, lambda=0.006115 
## - Fold2.Rep2: alpha=0.325, lambda=0.006115 
## + Fold2.Rep2: alpha=0.550, lambda=0.006115 
## - Fold2.Rep2: alpha=0.550, lambda=0.006115 
## + Fold2.Rep2: alpha=0.775, lambda=0.006115 
## - Fold2.Rep2: alpha=0.775, lambda=0.006115 
## + Fold2.Rep2: alpha=1.000, lambda=0.006115 
## - Fold2.Rep2: alpha=1.000, lambda=0.006115 
## + Fold3.Rep2: alpha=0.100, lambda=0.006115 
## - Fold3.Rep2: alpha=0.100, lambda=0.006115 
## + Fold3.Rep2: alpha=0.325, lambda=0.006115 
## - Fold3.Rep2: alpha=0.325, lambda=0.006115 
## + Fold3.Rep2: alpha=0.550, lambda=0.006115 
## - Fold3.Rep2: alpha=0.550, lambda=0.006115 
## + Fold3.Rep2: alpha=0.775, lambda=0.006115 
## - Fold3.Rep2: alpha=0.775, lambda=0.006115 
## + Fold3.Rep2: alpha=1.000, lambda=0.006115 
## - Fold3.Rep2: alpha=1.000, lambda=0.006115 
## + Fold1.Rep3: alpha=0.100, lambda=0.006115 
## - Fold1.Rep3: alpha=0.100, lambda=0.006115 
## + Fold1.Rep3: alpha=0.325, lambda=0.006115 
## - Fold1.Rep3: alpha=0.325, lambda=0.006115 
## + Fold1.Rep3: alpha=0.550, lambda=0.006115 
## - Fold1.Rep3: alpha=0.550, lambda=0.006115 
## + Fold1.Rep3: alpha=0.775, lambda=0.006115 
## - Fold1.Rep3: alpha=0.775, lambda=0.006115 
## + Fold1.Rep3: alpha=1.000, lambda=0.006115 
## - Fold1.Rep3: alpha=1.000, lambda=0.006115 
## + Fold2.Rep3: alpha=0.100, lambda=0.006115 
## - Fold2.Rep3: alpha=0.100, lambda=0.006115 
## + Fold2.Rep3: alpha=0.325, lambda=0.006115 
## - Fold2.Rep3: alpha=0.325, lambda=0.006115 
## + Fold2.Rep3: alpha=0.550, lambda=0.006115 
## - Fold2.Rep3: alpha=0.550, lambda=0.006115 
## + Fold2.Rep3: alpha=0.775, lambda=0.006115 
## - Fold2.Rep3: alpha=0.775, lambda=0.006115 
## + Fold2.Rep3: alpha=1.000, lambda=0.006115 
## - Fold2.Rep3: alpha=1.000, lambda=0.006115 
## + Fold3.Rep3: alpha=0.100, lambda=0.006115 
## - Fold3.Rep3: alpha=0.100, lambda=0.006115 
## + Fold3.Rep3: alpha=0.325, lambda=0.006115 
## - Fold3.Rep3: alpha=0.325, lambda=0.006115 
## + Fold3.Rep3: alpha=0.550, lambda=0.006115 
## - Fold3.Rep3: alpha=0.550, lambda=0.006115 
## + Fold3.Rep3: alpha=0.775, lambda=0.006115 
## - Fold3.Rep3: alpha=0.775, lambda=0.006115 
## + Fold3.Rep3: alpha=1.000, lambda=0.006115 
## - Fold3.Rep3: alpha=1.000, lambda=0.006115 
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00611 on full training set
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

!

##             Length Class      Mode     
## a0           51    -none-     numeric  
## beta        153    dgCMatrix  S4       
## df           51    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       51    -none-     numeric  
## dev.ratio    51    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        3    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.101756e+00 -1.662730e-07  1.734079e+00 
## [1] "max lambda < lambdaOpt:"
##   (Intercept)          .pos     patch.cor 
## -4.118506e+00 -2.038794e-07  1.769523e+00

!

##                  Prediction
## Reference          .none left_eye_center
##   .none                0          169186
##   left_eye_center      0            7039
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.03994325     0.00000000     0.03903370     0.04086798     0.96005675 
## AccuracyPValue  McnemarPValue 
##     1.00000000     0.00000000 
##                  id                 feats max.nTuningRuns
## 1 Final##rcv#glmnet patch.cor,.pos,.rnorm              15
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                     87.084                 1.336             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0       0.6164675                      0
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1      0.07681814        0.9600567             0.0390337
##   max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1            0.04086798             0       8.007208e-06               0
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 20 fit.data.training          9          0           0 544.920 739.011
## 21 fit.data.training          9          1           1 739.012      NA
##    elapsed
## 20 194.091
## 21      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glb_fin_mdl_id)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glb_fin_mdl_id)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, :
## Using default probability threshold: 0
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##           All.X..rcv.glmnet.imp Final..rcv.glmnet.imp          imp
## patch.cor          1.000000e+02          1.000000e+02 1.000000e+02
## .pos               1.778514e-05          1.115447e-05 1.115447e-05
## .rnorm             0.000000e+00          0.000000e+00 0.000000e+00
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id, 
            prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id)                  

!!

## [1] "Min/Max Boundaries: "
##        ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 1 Train#1456#72#27      .none                                NA
## 2 Train#7049#68#45      .none                        0.02594052
## 3 Train#0001#64#37      .none                        0.05081097
## 4 Train#5462#63#38      .none                        0.08837756
##   label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 1                         <NA>                               NA
## 2              left_eye_center                             TRUE
## 3              left_eye_center                             TRUE
## 4              left_eye_center                             TRUE
##   label.fctr.All.X..rcv.glmnet.err.abs label.fctr.All.X..rcv.glmnet.is.acc
## 1                                   NA                                  NA
## 2                           0.02594052                               FALSE
## 3                           0.05081097                               FALSE
## 4                           0.08837756                               FALSE
##   label.fctr.Final..rcv.glmnet.prob label.fctr.Final..rcv.glmnet
## 1                       0.004591702              left_eye_center
## 2                       0.029152470              left_eye_center
## 3                       0.048683104              left_eye_center
## 4                       0.076877350              left_eye_center
##   label.fctr.Final..rcv.glmnet.err label.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                          0.004591702
## 2                             TRUE                          0.029152470
## 3                             TRUE                          0.048683104
## 4                             TRUE                          0.076877350
##   label.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
##   label.fctr.Final..rcv.glmnet.accurate label.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                        0.004591702
## 2                                 FALSE                        0.029152470
## 3                                 FALSE                        0.048683104
## 4                                 FALSE                        0.076877350
##             .label
## 1 Train#1456#72#27
## 2 Train#7049#68#45
## 3 Train#0001#64#37
## 4 Train#5462#63#38
## [1] "Inaccurate: "
##        ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 1 Train#1456#72#27      .none                                NA
## 2 Train#1456#73#27      .none                       0.002559345
## 3 Train#1456#73#26      .none                                NA
## 4 Train#1456#71#27      .none                       0.002585633
## 5 Train#1456#72#26      .none                                NA
## 6 Train#1456#74#26      .none                       0.002613572
##   label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 1                         <NA>                               NA
## 2              left_eye_center                             TRUE
## 3                         <NA>                               NA
## 4              left_eye_center                             TRUE
## 5                         <NA>                               NA
## 6              left_eye_center                             TRUE
##   label.fctr.All.X..rcv.glmnet.err.abs label.fctr.All.X..rcv.glmnet.is.acc
## 1                                   NA                                  NA
## 2                          0.002559345                               FALSE
## 3                                   NA                                  NA
## 4                          0.002585633                               FALSE
## 5                                   NA                                  NA
## 6                          0.002613572                               FALSE
##   label.fctr.Final..rcv.glmnet.prob label.fctr.Final..rcv.glmnet
## 1                       0.004591702              left_eye_center
## 2                       0.004597551              left_eye_center
## 3                       0.004605648              left_eye_center
## 4                       0.004634884              left_eye_center
## 5                       0.004654704              left_eye_center
## 6                       0.004674477              left_eye_center
##   label.fctr.Final..rcv.glmnet.err label.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                          0.004591702
## 2                             TRUE                          0.004597551
## 3                             TRUE                          0.004605648
## 4                             TRUE                          0.004634884
## 5                             TRUE                          0.004654704
## 6                             TRUE                          0.004674477
##   label.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
## 5                               FALSE
## 6                               FALSE
##   label.fctr.Final..rcv.glmnet.accurate label.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                        0.004591702
## 2                                 FALSE                        0.004597551
## 3                                 FALSE                        0.004605648
## 4                                 FALSE                        0.004634884
## 5                                 FALSE                        0.004654704
## 6                                 FALSE                        0.004674477
##             ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 62129  Train#0557#66#34      .none                        0.03387065
## 75087  Train#3496#69#40      .none                        0.03665620
## 85292  Train#2229#67#38      .none                        0.03923281
## 112646 Train#6976#65#37      .none                                NA
## 136623 Train#4929#69#36      .none                                NA
## 151755 Train#5220#66#40      .none                        0.05850867
##        label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 62129               left_eye_center                             TRUE
## 75087               left_eye_center                             TRUE
## 85292               left_eye_center                             TRUE
## 112646                         <NA>                               NA
## 136623                         <NA>                               NA
## 151755              left_eye_center                             TRUE
##        label.fctr.All.X..rcv.glmnet.err.abs
## 62129                            0.03387065
## 75087                            0.03665620
## 85292                            0.03923281
## 112646                                   NA
## 136623                                   NA
## 151755                           0.05850867
##        label.fctr.All.X..rcv.glmnet.is.acc
## 62129                                FALSE
## 75087                                FALSE
## 85292                                FALSE
## 112646                                  NA
## 136623                                  NA
## 151755                               FALSE
##        label.fctr.Final..rcv.glmnet.prob label.fctr.Final..rcv.glmnet
## 62129                         0.03534657              left_eye_center
## 75087                         0.03794227              left_eye_center
## 85292                         0.03989812              left_eye_center
## 112646                        0.04526244              left_eye_center
## 136623                        0.05062850              left_eye_center
## 151755                        0.05525637              left_eye_center
##        label.fctr.Final..rcv.glmnet.err
## 62129                              TRUE
## 75087                              TRUE
## 85292                              TRUE
## 112646                             TRUE
## 136623                             TRUE
## 151755                             TRUE
##        label.fctr.Final..rcv.glmnet.err.abs
## 62129                            0.03534657
## 75087                            0.03794227
## 85292                            0.03989812
## 112646                           0.04526244
## 136623                           0.05062850
## 151755                           0.05525637
##        label.fctr.Final..rcv.glmnet.is.acc
## 62129                                FALSE
## 75087                                FALSE
## 85292                                FALSE
## 112646                               FALSE
## 136623                               FALSE
## 151755                               FALSE
##        label.fctr.Final..rcv.glmnet.accurate
## 62129                                  FALSE
## 75087                                  FALSE
## 85292                                  FALSE
## 112646                                 FALSE
## 136623                                 FALSE
## 151755                                 FALSE
##        label.fctr.Final..rcv.glmnet.error
## 62129                          0.03534657
## 75087                          0.03794227
## 85292                          0.03989812
## 112646                         0.04526244
## 136623                         0.05062850
## 151755                         0.05525637
##             ImageId.x.y label.fctr label.fctr.All.X..rcv.glmnet.prob
## 169181 Train#3192#65#38      .none                        0.08708544
## 169182 Train#5178#66#40      .none                        0.08680486
## 169183 Train#5462#64#38      .none                                NA
## 169184 Train#3808#67#36      .none                        0.08835226
## 169185 Train#3808#68#36      .none                                NA
## 169186 Train#5462#63#38      .none                        0.08837756
##        label.fctr.All.X..rcv.glmnet label.fctr.All.X..rcv.glmnet.err
## 169181              left_eye_center                             TRUE
## 169182              left_eye_center                             TRUE
## 169183                         <NA>                               NA
## 169184              left_eye_center                             TRUE
## 169185                         <NA>                               NA
## 169186              left_eye_center                             TRUE
##        label.fctr.All.X..rcv.glmnet.err.abs
## 169181                           0.08708544
## 169182                           0.08680486
## 169183                                   NA
## 169184                           0.08835226
## 169185                                   NA
## 169186                           0.08837756
##        label.fctr.All.X..rcv.glmnet.is.acc
## 169181                               FALSE
## 169182                               FALSE
## 169183                                  NA
## 169184                               FALSE
## 169185                                  NA
## 169186                               FALSE
##        label.fctr.Final..rcv.glmnet.prob label.fctr.Final..rcv.glmnet
## 169181                        0.07550918              left_eye_center
## 169182                        0.07571889              left_eye_center
## 169183                        0.07636838              left_eye_center
## 169184                        0.07651743              left_eye_center
## 169185                        0.07654064              left_eye_center
## 169186                        0.07687735              left_eye_center
##        label.fctr.Final..rcv.glmnet.err
## 169181                             TRUE
## 169182                             TRUE
## 169183                             TRUE
## 169184                             TRUE
## 169185                             TRUE
## 169186                             TRUE
##        label.fctr.Final..rcv.glmnet.err.abs
## 169181                           0.07550918
## 169182                           0.07571889
## 169183                           0.07636838
## 169184                           0.07651743
## 169185                           0.07654064
## 169186                           0.07687735
##        label.fctr.Final..rcv.glmnet.is.acc
## 169181                               FALSE
## 169182                               FALSE
## 169183                               FALSE
## 169184                               FALSE
## 169185                               FALSE
## 169186                               FALSE
##        label.fctr.Final..rcv.glmnet.accurate
## 169181                                 FALSE
## 169182                                 FALSE
## 169183                                 FALSE
## 169184                                 FALSE
## 169185                                 FALSE
## 169186                                 FALSE
##        label.fctr.Final..rcv.glmnet.error
## 169181                         0.07550918
## 169182                         0.07571889
## 169183                         0.07636838
## 169184                         0.07651743
## 169185                         0.07654064
## 169186                         0.07687735


dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "label.fctr.Final..rcv.glmnet.prob"   
## [2] "label.fctr.Final..rcv.glmnet"        
## [3] "label.fctr.Final..rcv.glmnet.err"    
## [4] "label.fctr.Final..rcv.glmnet.err.abs"
## [5] "label.fctr.Final..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1


glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 21 fit.data.training          9          1           1 739.012 831.352
## 22  predict.data.new         10          0           0 831.353      NA
##    elapsed
## 21   92.34
## 22      NA

Step 10.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0

## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0
## Warning: Removed 44575 rows containing missing values (geom_point).

## Warning: Removed 44575 rows containing missing values (geom_point).


## Warning: Removed 44575 rows containing missing values (geom_point).

## Warning: Removed 44575 rows containing missing values (geom_point).


## Warning: Removed 44575 rows containing missing values (geom_point).

## Warning: Removed 44575 rows containing missing values (geom_point).


## NULL
## Loading required package: stringr
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs label.fctr.All.X..rcv.glmnet left_eye_center: min < min of Train range: 6"
##            ImageId.x.y label.fctr.All.X..rcv.glmnet  .pos  patch.cor
## 36378 Train#1456#72#26              left_eye_center 36378 -0.7050562
## 36379 Train#1456#73#26              left_eye_center 36379 -0.7110947
## 36381 Train#1456#70#27              left_eye_center 36381 -0.6990148
## 36383 Train#1456#72#27              left_eye_center 36383 -0.7128227
## 38716 Train#1549#76#48              left_eye_center 38716 -0.6650801
## 7     Train#0001#65#38              left_eye_center     7  0.7312330
##                  id        cor.y exclude.as.feat    cor.y.abs cor.high.X
## .pos           .pos 0.0002125396           FALSE 0.0002125396         NA
## patch.cor patch.cor 0.0724656167           FALSE 0.0724656167         NA
##           freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## .pos        1.00000     100.00000   FALSE FALSE             TRUE
## patch.cor  24.16667      93.08384   FALSE FALSE            FALSE
##           interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## .pos                    NA         8.469806e-37       FALSE     NA      NA
## patch.cor               NA         1.897714e-35       FALSE     NA      NA
##                    max        min max.label.fctr..none
## .pos      2.208000e+05  1.0000000         1.762250e+05
## patch.cor 9.417145e-01 -0.7128227         9.398012e-01
##           max.label.fctr.left_eye_center min.label.fctr..none
## .pos                        1.762130e+05            1.0000000
## patch.cor                   9.269967e-01           -0.7120971
##           min.label.fctr.left_eye_center
## .pos                           13.000000
## patch.cor                      -0.636034
##           max.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                          1.762230e+05
## patch.cor                                     9.357202e-01
##           min.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                             7.0000000
## patch.cor                                       -0.7128227
##           max.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          2.208000e+05
## patch.cor                                     9.417145e-01
##           min.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          1.762260e+05
## patch.cor                                    -6.451593e-01
## [1] "OOBobs label.fctr.All.X..rcv.glmnet left_eye_center: max > max of Train range: 4"
##             ImageId.x.y label.fctr.All.X..rcv.glmnet   .pos patch.cor
## 176223 Train#7049#66#45              left_eye_center 176223 0.3562050
## 95187  Train#3808#68#36              left_eye_center  95187 0.9324911
## 136543 Train#5462#64#38              left_eye_center 136543 0.9357202
## 166900 Train#6676#62#38              left_eye_center 166900 0.9285011
##                  id        cor.y exclude.as.feat    cor.y.abs cor.high.X
## .pos           .pos 0.0002125396           FALSE 0.0002125396         NA
## patch.cor patch.cor 0.0724656167           FALSE 0.0724656167         NA
##           freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## .pos        1.00000     100.00000   FALSE FALSE             TRUE
## patch.cor  24.16667      93.08384   FALSE FALSE            FALSE
##           interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## .pos                    NA         8.469806e-37       FALSE     NA      NA
## patch.cor               NA         1.897714e-35       FALSE     NA      NA
##                    max        min max.label.fctr..none
## .pos      2.208000e+05  1.0000000         1.762250e+05
## patch.cor 9.417145e-01 -0.7128227         9.398012e-01
##           max.label.fctr.left_eye_center min.label.fctr..none
## .pos                        1.762130e+05            1.0000000
## patch.cor                   9.269967e-01           -0.7120971
##           min.label.fctr.left_eye_center
## .pos                           13.000000
## patch.cor                      -0.636034
##           max.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                          1.762230e+05
## patch.cor                                     9.357202e-01
##           min.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                             7.0000000
## patch.cor                                       -0.7128227
##           max.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          2.208000e+05
## patch.cor                                     9.417145e-01
##           min.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          1.762260e+05
## patch.cor                                    -6.451593e-01
## [1] "OOBobs total range outliers: 10"
## [1] "newobs label.fctr.Final..rcv.glmnet left_eye_center: min < min of Train range: 1"
##            ImageId.x.y label.fctr.Final..rcv.glmnet  patch.cor
## 216601 Test#1616#64#35              left_eye_center -0.6451593
##                  id      cor.y exclude.as.feat  cor.y.abs cor.high.X
## patch.cor patch.cor 0.07246562           FALSE 0.07246562         NA
##           freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## patch.cor  24.16667      93.08384   FALSE FALSE            FALSE
##           interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## patch.cor               NA         1.897714e-35       FALSE     NA      NA
##                 max        min max.label.fctr..none
## patch.cor 0.9417145 -0.7128227            0.9398012
##           max.label.fctr.left_eye_center min.label.fctr..none
## patch.cor                      0.9269967           -0.7128227
##           min.label.fctr.left_eye_center
## patch.cor                      -0.636034
##           max.label.fctr.All.X..rcv.glmnet.left_eye_center
## patch.cor                                        0.9357202
##           min.label.fctr.All.X..rcv.glmnet.left_eye_center
## patch.cor                                       -0.7128227
##           max.label.fctr.Final..rcv.glmnet.left_eye_center
## patch.cor                                        0.9417145
##           min.label.fctr.Final..rcv.glmnet.left_eye_center
## patch.cor                                       -0.6451593
## [1] "newobs label.fctr.Final..rcv.glmnet left_eye_center: max > max of Train range: 44575"
##            ImageId.x.y label.fctr.Final..rcv.glmnet   .pos patch.cor
## 176226 Test#0001#64#35              left_eye_center 176226 0.1017430
## 176227 Test#0001#65#35              left_eye_center 176227 0.1198157
## 176228 Test#0001#66#35              left_eye_center 176228 0.1376269
## 176229 Test#0001#67#35              left_eye_center 176229 0.1351847
## 176230 Test#0001#68#35              left_eye_center 176230 0.1119015
## 176231 Test#0001#64#36              left_eye_center 176231 0.2769096
##            ImageId.x.y label.fctr.Final..rcv.glmnet   .pos patch.cor
## 177449 Test#0049#67#39              left_eye_center 177449 0.4978410
## 181143 Test#0197#66#38              left_eye_center 181143 0.2438163
## 184626 Test#0337#64#35              left_eye_center 184626 0.4955181
## 186713 Test#0420#66#37              left_eye_center 186713 0.5425848
## 186926 Test#0429#64#35              left_eye_center 186926 0.6133498
## 206592 Test#1215#65#38              left_eye_center 206592 0.4680468
##            ImageId.x.y label.fctr.Final..rcv.glmnet   .pos patch.cor
## 220795 Test#1783#68#38              left_eye_center 220795 0.7325438
## 220796 Test#1783#64#39              left_eye_center 220796 0.7587660
## 220797 Test#1783#65#39              left_eye_center 220797 0.7587045
## 220798 Test#1783#66#39              left_eye_center 220798 0.7638962
## 220799 Test#1783#67#39              left_eye_center 220799 0.7673279
## 220800 Test#1783#68#39              left_eye_center 220800 0.7623284
##                  id        cor.y exclude.as.feat    cor.y.abs cor.high.X
## .pos           .pos 0.0002125396           FALSE 0.0002125396         NA
## patch.cor patch.cor 0.0724656167           FALSE 0.0724656167         NA
##           freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## .pos        1.00000     100.00000   FALSE FALSE             TRUE
## patch.cor  24.16667      93.08384   FALSE FALSE            FALSE
##           interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## .pos                    NA         8.469806e-37       FALSE     NA      NA
## patch.cor               NA         1.897714e-35       FALSE     NA      NA
##                    max        min max.label.fctr..none
## .pos      2.208000e+05  1.0000000         1.762250e+05
## patch.cor 9.417145e-01 -0.7128227         9.398012e-01
##           max.label.fctr.left_eye_center min.label.fctr..none
## .pos                        1.762130e+05            1.0000000
## patch.cor                   9.269967e-01           -0.7128227
##           min.label.fctr.left_eye_center
## .pos                           13.000000
## patch.cor                      -0.636034
##           max.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                          1.762230e+05
## patch.cor                                     9.357202e-01
##           min.label.fctr.All.X..rcv.glmnet.left_eye_center
## .pos                                             7.0000000
## patch.cor                                       -0.7128227
##           max.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          2.208000e+05
## patch.cor                                     9.417145e-01
##           min.label.fctr.Final..rcv.glmnet.left_eye_center
## .pos                                          1.762260e+05
## patch.cor                                    -6.451593e-01
## [1] "newobs total range outliers: 44575"
## [1] 0
## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"
## [1] "glb_fin_mdl_id: Final##rcv#glmnet"
## [1] "Cross Validation issues:"
## MFO###myMFO_classfr 
##                   0
##                      max.Accuracy.OOB max.AUCROCR.OOB max.AUCpROC.OOB
## All.X##rcv#glmnet          0.03668822       0.5404133             0.5
## All.X##rcv#glm             0.03668822       0.5398062             0.5
## Max.cor.Y##rcv#rpart       0.03668822       0.5000000             0.5
## MFO###myMFO_classfr        0.03668822       0.5000000             0.5
## Final##rcv#glmnet                  NA              NA              NA
##                      max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet          0.95884588                      0
## All.X##rcv#glm             0.95884588                      0
## Max.cor.Y##rcv#rpart       0.95872131                      0
## MFO###myMFO_classfr        0.04115412                      0
## Final##rcv#glmnet          0.96005675                      0
##                      opt.prob.threshold.OOB
## All.X##rcv#glmnet                         0
## All.X##rcv#glm                            0
## Max.cor.Y##rcv#rpart                      0
## MFO###myMFO_classfr                       0
## Final##rcv#glmnet                        NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##                  Prediction
## Reference         .none left_eye_center
##   .none               0           46028
##   left_eye_center     0            1753
##           err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## (0.7,1]        3009.86272       493.53456       3391.7081              NA
## (0.5,0.7]      4972.21940      1265.33217       6216.8037              NA
## (0,0.5]        2031.94587      1518.01621       3731.8601              NA
## (-1,0]           50.00137        50.03252        109.9657              NA
##           .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## (0.7,1]       0.18522469     0.10627655     0.10355580  23791
## (0.5,0.7]     0.45239170     0.32718026     0.31881099  58107
## (0,0.5]       0.35079879     0.53542203     0.52457656  45058
## (-1,0]        0.01158482     0.03112116     0.05305665   1488
##           .n.New.left_eye_center .n.OOB .n.Trn..none
## (0.7,1]                     4616   5078        26921
## (0.5,0.7]                  14211  15633        70500
## (0,0.5]                    23383  25583        68862
## (-1,0]                      2365   1487         2903
##           .n.Trn.left_eye_center .n.Tst .n.fit .n.new .n.trn
## (0.7,1]                     1948   4616  23791   4616  28869
## (0.5,0.7]                   3240  14211  58107  14211  73740
## (0,0.5]                     1779  23383  45058  23383  70641
## (-1,0]                        72   2365   1488   2365   2975
##           err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## (0.7,1]         0.09719074       0.12651266               NA
## (0.5,0.7]       0.08093982       0.08557006               NA
## (0,0.5]         0.05933691       0.04509623               NA
## (-1,0]          0.03364661       0.03360307               NA
##           err.abs.trn.mean
## (0.7,1]         0.11748616
## (0.5,0.7]       0.08430708
## (0,0.5]         0.05282853
## (-1,0]          0.03696327
##        err.abs.fit.sum        err.abs.OOB.sum        err.abs.trn.sum 
##           1.006403e+04           3.326915e+03           1.345034e+04 
##        err.abs.new.sum         .freqRatio.Fit         .freqRatio.OOB 
##                     NA           1.000000e+00           1.000000e+00 
##         .freqRatio.Tst                 .n.Fit .n.New.left_eye_center 
##           1.000000e+00           1.284440e+05           4.457500e+04 
##                 .n.OOB           .n.Trn..none .n.Trn.left_eye_center 
##           4.778100e+04           1.691860e+05           7.039000e+03 
##                 .n.Tst                 .n.fit                 .n.new 
##           4.457500e+04           1.284440e+05           4.457500e+04 
##                 .n.trn       err.abs.OOB.mean       err.abs.fit.mean 
##           1.762250e+05           2.711141e-01           2.907820e-01 
##       err.abs.new.mean       err.abs.trn.mean 
##                     NA           2.915850e-01
## [1] "Features Importance for selected models:"
##           All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## patch.cor                   100                   100
## [1] "glbObsNew prediction stats:"
## 
##           .none left_eye_center 
##               0           44575
##                   label step_major step_minor label_minor     bgn     end
## 22     predict.data.new         10          0           0 831.353 914.927
## 23 display.session.info         11          0           0 914.928      NA
##    elapsed
## 22  83.575
## 23      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 17                fit.models          8          1           1 138.425
## 20         fit.data.training          9          0           0 544.920
## 21         fit.data.training          9          1           1 739.012
## 22          predict.data.new         10          0           0 831.353
## 16                fit.models          8          0           0  76.532
## 18                fit.models          8          2           2 466.977
## 1                import.data          1          0           0   8.439
## 19                fit.models          8          3           3 525.092
## 15           select.features          7          0           0  62.917
## 2               inspect.data          2          0           0  36.311
## 14   partition.data.training          6          0           0  53.486
## 3                 scrub.data          2          1           1  49.458
## 11      extract.features.end          3          6           6  51.657
## 12       manage.missing.data          4          0           0  52.587
## 13              cluster.data          5          0           0  53.270
## 9      extract.features.text          3          4           4  51.542
## 10   extract.features.string          3          5           5  51.599
## 7     extract.features.image          3          2           2  51.450
## 4             transform.data          2          2           2  51.348
## 6  extract.features.datetime          3          1           1  51.411
## 8     extract.features.price          3          3           3  51.505
## 5           extract.features          3          0           0  51.390
##        end elapsed duration
## 17 466.976 328.551  328.551
## 20 739.011 194.091  194.091
## 21 831.352  92.340   92.340
## 22 914.927  83.575   83.574
## 16 138.424  61.893   61.892
## 18 525.091  58.114   58.114
## 1   36.310  27.871   27.871
## 19 544.920  19.828   19.828
## 15  76.532  13.615   13.615
## 2   49.457  13.146   13.146
## 14  62.917   9.431    9.431
## 3   51.347   1.889    1.889
## 11  52.586   0.929    0.929
## 12  53.269   0.682    0.682
## 13  53.485   0.215    0.215
## 9   51.599   0.057    0.057
## 10  51.656   0.057    0.057
## 7   51.504   0.055    0.054
## 4   51.389   0.042    0.041
## 6   51.449   0.038    0.038
## 8   51.542   0.037    0.037
## 5   51.410   0.020    0.020
## [1] "Total Elapsed Time: 914.927 secs"


##                  label step_major step_minor label_minor     bgn     end
## 3   fit.models_1_All.X          1          2      glmnet 139.456 328.560
## 4   fit.models_1_All.X          1          3         glm 328.562 466.888
## 5 fit.models_1_preProc          1          4     preProc 466.889 466.965
## 1     fit.models_1_bgn          1          0       setup 139.436 139.447
## 2   fit.models_1_All.X          1          1       setup 139.448 139.455
##   elapsed duration
## 3 189.104  189.104
## 4 138.326  138.326
## 5   0.076    0.076
## 1   0.011    0.011
## 2   0.007    0.007
## [1] "Total Elapsed Time: 466.965 secs"